Echo cancellation (EC) is required in almost every device (e.g., cell phones, tablets, personal computers, BLUETOOTH brand communication headsets, conference speakerphones, and smart television etc.) receiving and/or producing sound to increase voice quality. Double talk detection is used in some existing systems to perform the EC. However, adaptation of EC in these existing systems is frozen in the presence of double talk. Some of the existing systems use variable step size (VSS) adaptive filtering to increase EC performance under background noise that is relatively low in level. However, the echo canceller in some of the existing systems does not converge when dense near-end double talk is detected, such as when a user at the near-end makes a phone call without turning off music thereby creating strong interference, at least because it is difficult to estimate interference when the echo canceller is not convergent. Further, in the case of low-level interference, the convergence slows down in the existing systems which leads to poor performance.
In particular, the approaches used by the existing systems to combat adaptive EC divergence in the presence of near-end speech explicitly use a double talk detector (DTD) and/or a dual-filter (e.g., foreground-background filter). While the DTD is an estimator with associated probability of miss and false alarms, the dual-filter adds a significant number of clock cycles to a processor and requires complex control logic to manage the two filters. Further, the performance of the existing systems is lacking in many environments and use cases such as when conferencing, using a speakerphone in loud and noisy environments, or other use cases where the echo path changes frequently.